Epiguard is an AI-driven health risk prediction and monitoring system designed to anticipate epileptic seizure risk through continuous analysis of physiological telemetry collected from wearable devices.
The system integrates multiple biometric signals such as heart rate, heart rate variability (HRV), oxygen saturation (SpO₂), respiratory patterns, body temperature, activity levels and stress indicators. By combining these signals with machine learning models, Epiguard aims to identify abnormal physiological patterns that may precede seizure events.
Unlike traditional seizure detection systems that focus on detecting events after they occur, Epiguard focuses on predictive analytics. The platform processes real-time data streams from wearable ecosystems including Garmin, Fitbit, Polar, Oura, Apple Health and other health telemetry providers.
The system architecture is designed with privacy-first principles: sensitive health data can be processed locally or through secure APIs, reducing the need for centralized storage and improving data protection.
Key innovation areas include:
• AI-based anomaly detection for physiological signal patterns
• Cross-platform wearable data aggregation
• Real-time health risk scoring dashboard
• Predictive analytics applied to neurological conditions
The project explores the intersection between cybersecurity-grade telemetry processing and health monitoring systems. By applying techniques commonly used in security analytics and anomaly detection to physiological data streams, Epiguard demonstrates how security architectures can be adapted to support medical risk prediction.
The long-term objective of the project is to contribute to safer health monitoring systems and advance research on predictive models for neurological conditions, potentially improving quality of life for individuals affected by epilepsy.
Epiguard represents an innovative approach where AI, wearable telemetry and secure data architectures converge to build next-generation predictive health monitoring technologies.
